Titlebook: Nature-Inspired Computation in Engineering; Xin-She Yang Book 2016 Springer International Publishing Switzerland 2016 Bat Algorithm.Bio-in

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Parameterless Bat Algorithm and Its Performance Study,is that user does not need to specify the control parameters when running this algorithm. Thus, this bat algorithm variant can have wide usability in solving real-world optimization problems. In this chapter, a preliminary study of the proposed parameterless bat algorithm is presented.

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Nature-Inspired Optimization Algorithms in Engineering: Overview and Applications,ciently efficient to deal with highly nonlinear optimization problems. In this chapter, we first review the brief history of nature-inspired optimization algorithms, followed by the introduction of a few recent algorithms based on swarm intelligence. Then, we analyze the key characteristics of optim

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An Evolutionary Discrete Firefly Algorithm with Novel Operators for Solving the Vehicle Routing ProTime Windows (VRPTW). The contribution of this work is not only the adaptation of the EDFA to the VRPTW, but also with some novel route optimization operators. These operators incorporate the process of minimizing the number of routes for a solution in the search process where node selective extract

notion

The Plant Propagation Algorithm for Discrete Optimisation: The Case of the Travelling Salesman Probn discrete optimization and particularly on the well known Travelling Salesman Problem (TSP). This investigation concerns the implementation of the idea of short and long runners when searching for Hamiltonian cycles in complete graphs. The approach uses the notion of k-optimality. The performance o

Ventricle

Enhancing Cooperative Coevolution with Surrogate-Assisted Local Search,omain. However, so far the optimization of high-dimensional functions that are also computationally expensive has attracted little research. To address such an issue, this chapter describes an approach in which fitness surrogates are exploited to enhance local search (LS) within the low-dimensional

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Clustering Optimization for WSN Based on Nature-Inspired Algorithms,e purpose of cluster head selection. Life time of WSNs is always the main performance goal. Cluster head (CH) selection is one of the factors affecting the life time of WSNs and hence it is a very promising area of research. Swarm-intelligence is a very hot area of research which mimics natural beha